Today's conventional hearing aids typically comprise a Digital Signal Processor (DSP) for processing of sound received by the hearing aid for compensation of the user's hearing loss. As is well known in the art, the processing of the DSP is controlled by a signal processing algorithm having various parameters for adjustment of the actual signal processing performed. The gains in each of the frequency channels of a multi-channel hearing aid are examples of such parameters.
The flexibility of the DSP is often utilized to provide a plurality of different algorithms and/or a plurality of sets of parameters of a specific algorithm. For example, various algorithms may be provided for noise suppression, i.e. attenuation of undesired signals and amplification of desired signals. Desired signals are usually speech or music, and undesired signals can be background speech, restaurant clatter, music (when speech is the desired signal), traffic noise, etc.
The different algorithms or parameter sets are typically included to provide comfortable and intelligible reproduced sound quality in different sound environments, such as speech, babble speech, restaurant clatter, music, traffic noise, etc. Audio signals obtained from different sound environments may possess very different characteristics, e.g. average and maximum sound pressure levels (SPLs) and/or frequency content. Therefore, in a hearing aid with a DSP, each type of sound environment may be associated with a particular program wherein a particular setting of algorithm parameters of a signal processing algorithm provides processed sound of optimum signal quality in a specific sound environment. A set of such parameters may typically include parameters related to broadband gain, corner frequencies or slopes of frequency-selective filter algorithms and parameters controlling e.g. knee-points and compression ratios of Automatic Gain Control (AGC) algorithms.
Consequently, today's DSP based hearing instruments are usually provided with a number of different programs, each program tailored to a particular sound environment category and/or particular user preferences. Signal processing characteristics of each of these programs is typically determined during an initial fitting session in a dispenser's office and programmed into the instrument by activating corresponding algorithms and algorithm parameters in a non-volatile memory area of the hearing aid and/or transmitting corresponding algorithms and algorithm parameters to the non-volatile memory area.
Some known hearing aids are capable of automatically classifying the user's sound environment into one of a number of relevant or typical everyday sound environment categories, such as speech, babble speech, restaurant clatter, music, traffic noise, etc.
Obtained classification results may be utilised in the hearing aid to automatically select signal processing characteristics of the hearing aid, e.g. to automatically switch to the most suitable algorithm for the environment in question. Such a hearing aid will be able to maintain optimum sound quality and/or speech intelligibility for the individual hearing aid user in various sound environments.
U.S. Pat. No. 5,687,241 discloses a multi-channel DSP based hearing instrument that utilises continuous determination or calculation of one or several percentile values of input signal amplitude distributions to discriminate between speech and noise input signals. Gain values in each of a number of frequency channels are adjusted in response to detected levels of speech and noise.
However, it is often desirable to provide a more subtle characterization of a sound environment than only discriminating between speech and noise. As an example, it may be desirable to switch between an omni-directional and a directional microphone preset program in dependence of, not just the level of background noise, but also on further signal characteristics of this background noise. In situations where the user of the hearing aid communicates with another individual in the presence of the background noise, it would be beneficial to be able to identify and classify the type of background noise. Omni-directional operation could be selected in the event that the noise being traffic noise to allow the user to clearly hear approaching traffic independent of its direction of arrival. If, on the other hand, the background noise was classified as being babble-noise, the directional listening program could be selected to allow the user to hear a target speech signal with improved signal-to-noise ratio (SNR) during a conversation.
Applying Hidden Markov Models for analysis and classification of the microphone signal may obtain a detailed characterisation of e.g. a microphone signal. Hidden Markov Models are capable of modelling stochastic and non-stationary signals in terms of both short and long time temporal variations. Hidden Markov Models have been applied in speech recognition as a tool for modelling statistical properties of speech signals. The article “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition”, published in Proceedings of the IEEE, VOL 77, No. 2, February 1989 contains a comprehensive description of the application of Hidden Markov Models to problems in speech recognition.
WO 01/76321 discloses a hearing aid that provides automatic identification or classification of a sound environment by applying one or several predetermined Hidden Markov Models to process acoustic signals obtained from the listening environment. The hearing aid may utilise determined classification results to control parameter values of a signal processing algorithm or to control switching between different algorithms so as to optimally adapt the signal processing of the hearing aid to a given sound environment.
The different available signal processing algorithms may change the signal characteristics significantly. In binaural hearing aid systems, it is therefore important that the determination of sound environment does not differ for the two hearing aids. However, since sound characteristics may differ significantly at the two ears of a user, it will often occur that sound environment determination at the two ears of a user differs, and this leads to undesired different signal processing of sounds for each of the ears of the user.